Robot Instruction through Bayesian Approaches to Laban-based Manipulative Action Characterization
Project Type: PhD Project
Research Field: Human and Robotic Dexterous Manipulation
Time span: 12/2010-6/2015
Poster

Description:
This project seeks to develop a hierarchical Bayesian framework to model human-like grasp behaviors and corresponding action sequences given labeled data resulting from LHMA (Laban-based Human Motion Analysis) components. This framework will allow a robotic system to learn by demonstration how to classify the different grasps employed to manipulate different objects and consequently reproduce them using computer vision and an artificial hand.

Related Projects

HANDLE - Developmental Pathway towards Autonomy and Dexterity in Robot In-Hand Manipulation

Related People

Jorge Dias
Jorge Lobo
Jafar Hosseini